Stock Market Prediction
32 papers with code • 3 benchmarks • 3 datasets
StockNet
Most implemented papers
Sentiment Analysis of Twitter Data for Predicting Stock Market Movements
harishpuvvada/BitCoin-Value-Predictor
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In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyze the correlation between stock market movements of a company and sentiments in tweets.
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
AI4Finance-Foundation/FinRL
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In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.
Twitter mood predicts the stock market
A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values.
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model
Predicting the price correlation of two assets for future time periods is important in portfolio optimization.
Temporal Relational Ranking for Stock Prediction
hennande/Temporal_Relational_Stock_Ranking
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Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
dmis-lab/hats
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Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy.
Forecasting directional movements of stock prices for intraday trading using LSTM and random forests
pushpendughosh/Stock-market-forecasting
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Hence we outperform the single-feature setting in Fischer & Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0. 41% and of 0. 39% with respect to LSTM and random forests, respectively.
Artificial Counselor System for Stock Investment
This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment.
Qlib: An AI-oriented Quantitative Investment Platform
microsoft/qlib
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Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
Sentiment Predictability for Stocks
In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.
